Design and analysis of a decision intelligent system based on enzymatic numerical technology

A P system is a finite, discrete and distributed model with a parallel-layered and net structure. The enzymatic numerical P system (ENPS) has enzyme-like variables that allow each membrane to host more than one production function, and it has been widely used in economics and controllers for autonomous mobile robots. Although the ENPS has shown powerful abilities in numerical calculation, as a decisional process, it had not been able to express its mechanism of evolution, and the existing P system has a limited ability to process the assigned cells in different evolutions. Furthermore, the present decision models have a limited ability to process large-scale decisional tasks. To set up the decisional P system theory and render the existing ENPS more flexible, we present a restriction enzyme to found a conditional enzymatic numerical P system. In this system, we present a series of decisional enzymes and rebuild the structures of cells to achieve a decisional mechanism. Finally, we verify the validation and efficiency of our model by simulating experiments. To the best of our knowledge, we are proposing a decisional P system for the first time, and the results show that this system is very logical and efficiently processes large-scale decisional tasks. Indeed, the conditional enzymatic numerical P system could achieve prior results more quikly by a factor of 188.28, and a decision tree based on DENPS is the 119.85 times faster than the general serial framework.